Scalable object-based video retrieval in HD video databases

Morand, C.; Benois-Pineau, Jenny; Domenger, Jean‐Philippe; Zepeda, Joaquin; Kijak, Ewa; Guillemot, Christine

doi:10.1016/j.image.2010.04.004

Cited by 14 publications

(5 citation statements)

References 32 publications

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“…An example may be an image, keywords, sketch, object, video, video frame etc., [4]. In the literature we found retrieval of videos based on an object [5], frame [6], video [2,[7][8][9], keywords [10]. For the retrieval of videos the features and algorithms such as optical flow tensor and Hidden Markov Models (HMMs) [7], the multi-modal spectral clustering and ranking algorithm [8], block wise intensity comparison [2], Scale Invariant Feature Transform (SIFT) [11], Bag-of-Features [12], dynamic weighted similarity measure with color and edge descriptors [9] are used.…”

Section: Related Workmentioning

confidence: 99%

Retrieval of Flower Videos Based on a Query With Multiple Species of Flowers

Aradhya¹,

Vk²,

Kumar³

et al. 2021

Preprint

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Searching, recognizing and retrieving a video of interest from a large collection of a video data is an instantaneous requirement. This requirement has been recognized as an active area of research in computer vision, machine learning and pattern recognition. Flower video recognition and retrieval is vital in the field of floriculture and horticulture. In this paper we propose a model for the retrieval of videos of flowers. Initially, videos are represented with keyframes and flowers in keyframes are segmented from their background. Then, the model is analysed by features extracted from flower regions of the keyframe. A Linear Discriminant Analysis (LDA) is adapted for the extraction of discriminating features. Multiclass Support Vector Machine (MSVM) classifier is applied to identify the class of the query video. Experiments have been conducted on relatively large dataset of our own, consisting of 7788 videos of 30 different species of flowers captured from three different devices. Generally, retrieval of flower videos is addressed by the use of a query video consisting of a flower of a single species. In this work we made an attempt to develop a system consisting of retrieval of similar videos for a query video consisting of flowers of different species.

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Section: Related Workmentioning

confidence: 99%

Retrieval of Flower Videos Based on a Query With Multiple Species of Flowers

Aradhya¹,

Vk²,

Kumar³

et al. 2021

Preprint

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“…The different approach is the one proposed by Morand et al [21]. This approach introduced scalable indexing of video content by objects without parsing them into their constituent elements.…”

Section: Analogy Between Information Retrieval and Cbirmentioning

confidence: 99%

Toward a higher-level visual representation for content-based image retrieval

Sayad

Martinet

Urruty

et al. 2010

Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia

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Having effective methods to access the desired images is essential nowadays with the availability of a huge amount of digital images. The proposed approach is based on an analogy between content-based image retrieval and text retrieval. The aim of the approach is to build a meaningful mid-level representation of images to be used later on for matching between a query image and other images in the desired database. The approach is based firstly on constructing different visual words using local patch extraction and fusion of descriptors. Secondly, we introduce a new method using multilayer pLSA to eliminate the noisiest words generated by the vocabulary building process. Thirdly, a new spatial weighting scheme is introduced that consists of weighting visual words according to the probability of each visual word to belong to each of the n Gaussian. Finally, we construct visual phrases from groups of visual words that are involved in strong association rules. Experimental results show that our approach outperforms the results of traditional image retrieval techniques.

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“…The effort of research on image search and retrieval was greatly increased with the popularity of digital cameras and phone cameras. Some efforts, which may not cover the complete scope of JPSearch Frame-work, related to parts of JPSearch standard can be found in [3] [4] [5] [6] [7] [8].…”

Section: Introductionmentioning

confidence: 99%

JPSearch: New international standard providing interoperable framework for image search and sharing

Yoon¹,

Kim²,

Park³

et al. 2012

Signal Processing: Image Communication

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For the last 15 years, there has been various research and great advances in the field of content-based search and retrieval of images. In this paper, we present an overview of the JPSearch Standard, which is a recently published set of international standards providing interoperable framework for image search and sharing. The JPSearch standard is composed of six parts, which are Part 1: System framework and components, Part 2: Registration, identification and management of schema and ontology, Part 3: Query format, Part 4: File format for metadata embedded in image data (JPEG and JPEG 2000), Part 5: Data interchange format between image repositories, Part 6: Reference software. The Part 1 provides motivation and overview of the JPSearch framework. The Part 2 supports interoperability among various metadata specifications and social tagging using the specification of core metadata and translation rule description language. The Part 3 provides powerful image query language for interoperability among multiple image databases. The Part 4 specifies a file format based on JPEG and JPEG 2000 file format in which arbitrary number of metadata description of a image can be embedded. The Part 5 supports interoperability in interchanging images with metadata between image repositories. Finally, the Part 6 provides reference and utility softwares based on which the JPSearch compliant systems can be easily developed. In this paper, summaries and usage examples for each part of the JPSearch standard are presented.

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Scalable object-based video retrieval in HD video databases

Cited by 14 publications

References 32 publications

Retrieval of Flower Videos Based on a Query With Multiple Species of Flowers

Retrieval of Flower Videos Based on a Query With Multiple Species of Flowers

Toward a higher-level visual representation for content-based image retrieval

JPSearch: New international standard providing interoperable framework for image search and sharing

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